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Can the NVIDIA GeForce RTX 5090 run Dolphin 3.0 R1 Mistral 24B?

81 ptsGreat (A) · 81 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 22.9 GB of 32.0 GB usable, with an estimated speed of 64.8 tok/s.

Best quant
Q6_K
Generation speed
64.8 tok/s
Prompt processing
3,201 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Dolphin 3.0 R1 Mistral 24B

Recommended for the NVIDIA GeForce RTX 5090: Q6_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q6_K file (opens in a new tab)

huggingface.co/mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF:Q6_K

Original weights · Hugging Face

Full-precision files from Cognitive Computations, for fine-tuning, vLLM, Transformers.

huggingface.co/dphn/Dolphin3.0-R1-Mistral-24B (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.26.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total81.7 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 5090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 13.0 GB 32.0 GB 153 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q3_K_M 15.1 GB 32.0 GB 121 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF (opens in a new tab)
Q4_K_M 17.9 GB 32.0 GB 93.3 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 GGUF (opens in a new tab)
Q5_K_M 20.3 GB 32.0 GB 77.1 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.6/15 GGUF (opens in a new tab)
Q6_K 22.9 GB 32.0 GB 64.8 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 28.5 GB 32.0 GB 47.5 tok/s Tight fit 81 ptsGreat (A) · 81 of 100 points: Model quality 27.0/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 50.7 GB 32.0 GB 1.9 tok/s Heavy offload 49 ptsUsable (C) · 49 of 100 points: Model quality 27.0/45, Memory fit 4.0/25, Speed 3.3/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.